Overview

Dataset statistics

Number of variables40
Number of observations63697
Missing cells87873
Missing cells (%)3.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.4 MiB
Average record size in memory320.0 B

Variable types

Categorical23
Numeric17

Alerts

client_id has a high cardinality: 63697 distinct values High cardinality
customer_since_all has a high cardinality: 661 distinct values High cardinality
customer_since_bank has a high cardinality: 577 distinct values High cardinality
customer_birth_date has a high cardinality: 1092 distinct values High cardinality
homebanking_active is highly correlated with has_homebankingHigh correlation
has_homebanking is highly correlated with homebanking_active and 1 other fieldsHigh correlation
has_insurance_21 is highly correlated with bal_insurance_21High correlation
has_insurance_23 is highly correlated with bal_insurance_23High correlation
has_life_insurance_fixed_cap is highly correlated with cap_life_insurance_fixed_capHigh correlation
has_life_insurance_decreasing_cap is highly correlated with has_mortgage_loan and 2 other fieldsHigh correlation
has_fire_car_other_insurance is highly correlated with prem_fire_car_other_insuranceHigh correlation
has_personal_loan is highly correlated with bal_personal_loanHigh correlation
has_mortgage_loan is highly correlated with has_life_insurance_decreasing_cap and 2 other fieldsHigh correlation
has_current_account is highly correlated with has_homebanking and 1 other fieldsHigh correlation
has_pension_saving is highly correlated with bal_pension_savingHigh correlation
has_savings_account_starter is highly correlated with bal_savings_account_starterHigh correlation
has_current_account_starter is highly correlated with bal_current_account_starterHigh correlation
bal_insurance_21 is highly correlated with has_insurance_21High correlation
bal_insurance_23 is highly correlated with has_insurance_23High correlation
cap_life_insurance_fixed_cap is highly correlated with has_life_insurance_fixed_capHigh correlation
cap_life_insurance_decreasing_cap is highly correlated with has_life_insurance_decreasing_cap and 2 other fieldsHigh correlation
prem_fire_car_other_insurance is highly correlated with has_fire_car_other_insuranceHigh correlation
bal_personal_loan is highly correlated with has_personal_loanHigh correlation
bal_mortgage_loan is highly correlated with has_life_insurance_decreasing_cap and 2 other fieldsHigh correlation
bal_current_account is highly correlated with has_current_accountHigh correlation
bal_pension_saving is highly correlated with has_pension_savingHigh correlation
bal_savings_account_starter is highly correlated with has_savings_account_starterHigh correlation
bal_current_account_starter is highly correlated with has_current_account_starterHigh correlation
homebanking_active is highly correlated with has_homebankingHigh correlation
has_homebanking is highly correlated with homebanking_active and 1 other fieldsHigh correlation
has_insurance_21 is highly correlated with bal_insurance_21High correlation
has_insurance_23 is highly correlated with bal_insurance_23High correlation
has_life_insurance_fixed_cap is highly correlated with cap_life_insurance_fixed_capHigh correlation
has_life_insurance_decreasing_cap is highly correlated with has_mortgage_loan and 2 other fieldsHigh correlation
has_fire_car_other_insurance is highly correlated with prem_fire_car_other_insuranceHigh correlation
has_personal_loan is highly correlated with bal_personal_loanHigh correlation
has_mortgage_loan is highly correlated with has_life_insurance_decreasing_cap and 2 other fieldsHigh correlation
has_current_account is highly correlated with has_homebankingHigh correlation
has_pension_saving is highly correlated with bal_pension_savingHigh correlation
has_savings_account is highly correlated with bal_current_accountHigh correlation
has_savings_account_starter is highly correlated with bal_savings_account_starterHigh correlation
has_current_account_starter is highly correlated with bal_current_account_starterHigh correlation
bal_insurance_21 is highly correlated with has_insurance_21High correlation
bal_insurance_23 is highly correlated with has_insurance_23High correlation
cap_life_insurance_fixed_cap is highly correlated with has_life_insurance_fixed_capHigh correlation
cap_life_insurance_decreasing_cap is highly correlated with has_life_insurance_decreasing_cap and 2 other fieldsHigh correlation
prem_fire_car_other_insurance is highly correlated with has_fire_car_other_insuranceHigh correlation
bal_personal_loan is highly correlated with has_personal_loanHigh correlation
bal_mortgage_loan is highly correlated with has_life_insurance_decreasing_cap and 2 other fieldsHigh correlation
bal_current_account is highly correlated with has_savings_accountHigh correlation
bal_pension_saving is highly correlated with has_pension_savingHigh correlation
bal_savings_account_starter is highly correlated with has_savings_account_starterHigh correlation
bal_current_account_starter is highly correlated with has_current_account_starterHigh correlation
visits_distinct_so is highly correlated with visits_distinct_so_areasHigh correlation
visits_distinct_so_areas is highly correlated with visits_distinct_soHigh correlation
homebanking_active is highly correlated with has_homebankingHigh correlation
has_homebanking is highly correlated with homebanking_active and 1 other fieldsHigh correlation
has_insurance_21 is highly correlated with bal_insurance_21High correlation
has_insurance_23 is highly correlated with bal_insurance_23High correlation
has_life_insurance_fixed_cap is highly correlated with cap_life_insurance_fixed_capHigh correlation
has_life_insurance_decreasing_cap is highly correlated with has_mortgage_loan and 2 other fieldsHigh correlation
has_fire_car_other_insurance is highly correlated with prem_fire_car_other_insuranceHigh correlation
has_personal_loan is highly correlated with bal_personal_loanHigh correlation
has_mortgage_loan is highly correlated with has_life_insurance_decreasing_cap and 2 other fieldsHigh correlation
has_current_account is highly correlated with has_homebanking and 1 other fieldsHigh correlation
has_pension_saving is highly correlated with bal_pension_savingHigh correlation
has_savings_account_starter is highly correlated with bal_savings_account_starterHigh correlation
has_current_account_starter is highly correlated with bal_current_account_starterHigh correlation
bal_insurance_21 is highly correlated with has_insurance_21High correlation
bal_insurance_23 is highly correlated with has_insurance_23High correlation
cap_life_insurance_fixed_cap is highly correlated with has_life_insurance_fixed_capHigh correlation
cap_life_insurance_decreasing_cap is highly correlated with has_life_insurance_decreasing_cap and 2 other fieldsHigh correlation
prem_fire_car_other_insurance is highly correlated with has_fire_car_other_insuranceHigh correlation
bal_personal_loan is highly correlated with has_personal_loanHigh correlation
bal_mortgage_loan is highly correlated with has_life_insurance_decreasing_cap and 2 other fieldsHigh correlation
bal_current_account is highly correlated with has_current_accountHigh correlation
bal_pension_saving is highly correlated with has_pension_savingHigh correlation
bal_savings_account_starter is highly correlated with has_savings_account_starterHigh correlation
bal_current_account_starter is highly correlated with has_current_account_starterHigh correlation
customer_relationship is highly correlated with customer_childrenHigh correlation
has_homebanking is highly correlated with homebanking_active and 1 other fieldsHigh correlation
has_mortgage_loan is highly correlated with has_life_insurance_decreasing_capHigh correlation
has_life_insurance_decreasing_cap is highly correlated with has_mortgage_loanHigh correlation
homebanking_active is highly correlated with has_homebankingHigh correlation
customer_children is highly correlated with customer_relationshipHigh correlation
has_current_account is highly correlated with has_homebankingHigh correlation
homebanking_active is highly correlated with has_homebanking and 3 other fieldsHigh correlation
has_homebanking is highly correlated with homebanking_active and 3 other fieldsHigh correlation
has_insurance_21 is highly correlated with bal_insurance_21High correlation
has_insurance_23 is highly correlated with bal_insurance_23High correlation
has_life_insurance_fixed_cap is highly correlated with cap_life_insurance_fixed_capHigh correlation
has_life_insurance_decreasing_cap is highly correlated with homebanking_active and 4 other fieldsHigh correlation
has_fire_car_other_insurance is highly correlated with prem_fire_car_other_insuranceHigh correlation
has_personal_loan is highly correlated with bal_personal_loanHigh correlation
has_mortgage_loan is highly correlated with homebanking_active and 4 other fieldsHigh correlation
has_current_account is highly correlated with homebanking_active and 2 other fieldsHigh correlation
has_pension_saving is highly correlated with bal_pension_savingHigh correlation
has_savings_account is highly correlated with bal_current_account and 1 other fieldsHigh correlation
has_savings_account_starter is highly correlated with bal_savings_account_starterHigh correlation
has_current_account_starter is highly correlated with bal_current_account_starterHigh correlation
bal_insurance_21 is highly correlated with has_insurance_21High correlation
bal_insurance_23 is highly correlated with has_insurance_23High correlation
cap_life_insurance_fixed_cap is highly correlated with has_life_insurance_fixed_capHigh correlation
cap_life_insurance_decreasing_cap is highly correlated with has_life_insurance_decreasing_cap and 2 other fieldsHigh correlation
prem_fire_car_other_insurance is highly correlated with has_fire_car_other_insuranceHigh correlation
bal_personal_loan is highly correlated with has_personal_loanHigh correlation
bal_mortgage_loan is highly correlated with has_life_insurance_decreasing_cap and 2 other fieldsHigh correlation
bal_current_account is highly correlated with has_current_account and 2 other fieldsHigh correlation
bal_pension_saving is highly correlated with has_pension_savingHigh correlation
bal_savings_account is highly correlated with has_savings_account and 1 other fieldsHigh correlation
bal_savings_account_starter is highly correlated with has_savings_account_starterHigh correlation
bal_current_account_starter is highly correlated with has_current_account_starterHigh correlation
visits_distinct_so is highly correlated with visits_distinct_so_areasHigh correlation
visits_distinct_so_areas is highly correlated with visits_distinct_soHigh correlation
customer_occupation_code is highly correlated with customer_self_employedHigh correlation
customer_self_employed is highly correlated with customer_occupation_codeHigh correlation
customer_occupation_code has 2002 (3.1%) missing values Missing
customer_education has 47125 (74.0%) missing values Missing
customer_children has 23364 (36.7%) missing values Missing
customer_relationship has 14899 (23.4%) missing values Missing
cap_life_insurance_fixed_cap is highly skewed (γ1 = 48.2726678) Skewed
bal_current_account_starter is highly skewed (γ1 = 21.12371347) Skewed
client_id is uniformly distributed Uniform
client_id has unique values Unique
bal_insurance_21 has 57719 (90.6%) zeros Zeros
bal_insurance_23 has 63083 (99.0%) zeros Zeros
cap_life_insurance_fixed_cap has 63522 (99.7%) zeros Zeros
cap_life_insurance_decreasing_cap has 56589 (88.8%) zeros Zeros
prem_fire_car_other_insurance has 43753 (68.7%) zeros Zeros
bal_personal_loan has 61047 (95.8%) zeros Zeros
bal_mortgage_loan has 57449 (90.2%) zeros Zeros
bal_current_account has 36146 (56.7%) zeros Zeros
bal_pension_saving has 62389 (97.9%) zeros Zeros
bal_savings_account has 1930 (3.0%) zeros Zeros
bal_savings_account_starter has 63326 (99.4%) zeros Zeros
bal_current_account_starter has 62798 (98.6%) zeros Zeros
customer_education has 2178 (3.4%) zeros Zeros

Reproduction

Analysis started2022-03-19 23:50:00.034799
Analysis finished2022-03-19 23:51:34.248842
Duration1 minute and 34.21 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

client_id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct63697
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
910df42ad36243aa4ce16324cd7b15b0
 
1
430928063ed9e6128bf0843c1b76d655
 
1
8a035800d386ca328e42f91f3bd34f46
 
1
1cec47030db37dc49b734bad4b62cc15
 
1
70db2ef12ad7754c4cad43aff36397d5
 
1
Other values (63692)
63692 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63697 ?
Unique (%)100.0%

Sample

1st row910df42ad36243aa4ce16324cd7b15b0
2nd row4e19dc3a54323c5bbfc374664b950cd1
3rd rowf5d08db1b86c0cb0f566bf446cff1fb4
4th row26170ecf63653e215c52f4262c1c4859
5th rowc078009957dffb64f20e61b41220a976

Common Values

ValueCountFrequency (%)
910df42ad36243aa4ce16324cd7b15b01
 
< 0.1%
430928063ed9e6128bf0843c1b76d6551
 
< 0.1%
8a035800d386ca328e42f91f3bd34f461
 
< 0.1%
1cec47030db37dc49b734bad4b62cc151
 
< 0.1%
70db2ef12ad7754c4cad43aff36397d51
 
< 0.1%
7c3db4bc035ef61b497b45a170b28d701
 
< 0.1%
f07ce1239d3f86d60899b19a2d6415b51
 
< 0.1%
41a5faa1412905468343e9236320f1661
 
< 0.1%
049cb59ce12bd1f7bc85d3b35450f8d71
 
< 0.1%
e94231bda207b0c8e30a8b47f194c3dc1
 
< 0.1%
Other values (63687)63687
> 99.9%

Length

2022-03-20T00:51:34.438399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
910df42ad36243aa4ce16324cd7b15b01
 
< 0.1%
ebbf9840c425d75aa856d8d2f070bc0d1
 
< 0.1%
db53513a073326fb3d3cc256e3df3f471
 
< 0.1%
d68e579f5c95c38413b821533d96bdc91
 
< 0.1%
f5d08db1b86c0cb0f566bf446cff1fb41
 
< 0.1%
26170ecf63653e215c52f4262c1c48591
 
< 0.1%
c078009957dffb64f20e61b41220a9761
 
< 0.1%
f7bae3a0fefd323ecf7d4a2fab4e78261
 
< 0.1%
e1b2293260bd55bc20700f122d70480f1
 
< 0.1%
2063dc028a989d9752b9565bd42a4eff1
 
< 0.1%
Other values (63687)63687
> 99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

homebanking_active
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
0
49990 
1
13707 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
049990
78.5%
113707
 
21.5%

Length

2022-03-20T00:51:34.579069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-20T00:51:34.678137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
049990
78.5%
113707
 
21.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_homebanking
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
0
45802 
1
17895 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
045802
71.9%
117895
 
28.1%

Length

2022-03-20T00:51:34.789841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-20T00:51:34.886604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
045802
71.9%
117895
 
28.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_insurance_21
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
0
57644 
1
6053 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
057644
90.5%
16053
 
9.5%

Length

2022-03-20T00:51:34.988355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-20T00:51:35.094031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
057644
90.5%
16053
 
9.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_insurance_23
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
0
63063 
1
 
634

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
063063
99.0%
1634
 
1.0%

Length

2022-03-20T00:51:35.196187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-20T00:51:35.290953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
063063
99.0%
1634
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_life_insurance_fixed_cap
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
0
63522 
1
 
175

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
063522
99.7%
1175
 
0.3%

Length

2022-03-20T00:51:35.408004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-20T00:51:35.507351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
063522
99.7%
1175
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_life_insurance_decreasing_cap
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
0
56577 
1
7120 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
056577
88.8%
17120
 
11.2%

Length

2022-03-20T00:51:35.610112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-20T00:51:35.712386image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
056577
88.8%
17120
 
11.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_fire_car_other_insurance
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
0
43438 
1
20259 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
043438
68.2%
120259
31.8%

Length

2022-03-20T00:51:35.813480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-20T00:51:35.913018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
043438
68.2%
120259
31.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_personal_loan
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
0
61046 
1
 
2651

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
061046
95.8%
12651
 
4.2%

Length

2022-03-20T00:51:36.017798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-20T00:51:36.306644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
061046
95.8%
12651
 
4.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_mortgage_loan
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
0
57449 
1
6248 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
057449
90.2%
16248
 
9.8%

Length

2022-03-20T00:51:36.406825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-20T00:51:36.504290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
057449
90.2%
16248
 
9.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_current_account
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
1
31900 
0
31797 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
131900
50.1%
031797
49.9%

Length

2022-03-20T00:51:36.610951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-20T00:51:36.708291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
131900
50.1%
031797
49.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_pension_saving
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
0
62298 
1
 
1399

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
062298
97.8%
11399
 
2.2%

Length

2022-03-20T00:51:36.816980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-20T00:51:36.920726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
062298
97.8%
11399
 
2.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_savings_account
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
1
61847 
0
 
1850

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
161847
97.1%
01850
 
2.9%

Length

2022-03-20T00:51:37.025197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-20T00:51:37.125769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
161847
97.1%
01850
 
2.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_savings_account_starter
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
0
63320 
1
 
377

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
063320
99.4%
1377
 
0.6%

Length

2022-03-20T00:51:37.228981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-20T00:51:37.323183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
063320
99.4%
1377
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_current_account_starter
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
0
62553 
1
 
1144

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
062553
98.2%
11144
 
1.8%

Length

2022-03-20T00:51:37.428046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-20T00:51:37.531389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
062553
98.2%
11144
 
1.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

bal_insurance_21
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct987
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean457.9601865
Minimum0
Maximum10000
Zeros57719
Zeros (%)90.6%
Negative0
Negative (%)0.0%
Memory size497.8 KiB
2022-03-20T00:51:37.655504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4610
Maximum10000
Range10000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1641.970743
Coefficient of variation (CV)3.585400634
Kurtosis14.28661532
Mean457.9601865
Median Absolute Deviation (MAD)0
Skewness3.846945482
Sum29170690
Variance2696067.921
MonotonicityNot monotonic
2022-03-20T00:51:37.873295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
057719
90.6%
112028
 
< 0.1%
271022
 
< 0.1%
258022
 
< 0.1%
268021
 
< 0.1%
171021
 
< 0.1%
266019
 
< 0.1%
282018
 
< 0.1%
283017
 
< 0.1%
262017
 
< 0.1%
Other values (977)5793
 
9.1%
ValueCountFrequency (%)
057719
90.6%
101
 
< 0.1%
204
 
< 0.1%
303
 
< 0.1%
407
 
< 0.1%
505
 
< 0.1%
602
 
< 0.1%
7013
 
< 0.1%
804
 
< 0.1%
904
 
< 0.1%
ValueCountFrequency (%)
100001
 
< 0.1%
99902
 
< 0.1%
99702
 
< 0.1%
99603
< 0.1%
99502
 
< 0.1%
99404
< 0.1%
99303
< 0.1%
99201
 
< 0.1%
99006
< 0.1%
98903
< 0.1%

bal_insurance_23
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct399
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.43237515
Minimum0
Maximum9890
Zeros63083
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size497.8 KiB
2022-03-20T00:51:38.086118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9890
Range9890
Interquartile range (IQR)0

Descriptive statistics

Standard deviation536.9786303
Coefficient of variation (CV)11.32093066
Kurtosis180.0780522
Mean47.43237515
Median Absolute Deviation (MAD)0
Skewness12.90420201
Sum3021300
Variance288346.0494
MonotonicityNot monotonic
2022-03-20T00:51:38.291636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
063083
99.0%
25209
 
< 0.1%
25809
 
< 0.1%
50708
 
< 0.1%
25406
 
< 0.1%
25106
 
< 0.1%
50205
 
< 0.1%
24805
 
< 0.1%
46105
 
< 0.1%
51505
 
< 0.1%
Other values (389)556
 
0.9%
ValueCountFrequency (%)
063083
99.0%
901
 
< 0.1%
1001
 
< 0.1%
1101
 
< 0.1%
3801
 
< 0.1%
6001
 
< 0.1%
6201
 
< 0.1%
6701
 
< 0.1%
7301
 
< 0.1%
7501
 
< 0.1%
ValueCountFrequency (%)
98901
 
< 0.1%
98801
 
< 0.1%
98702
< 0.1%
98603
< 0.1%
98501
 
< 0.1%
98401
 
< 0.1%
98302
< 0.1%
97901
 
< 0.1%
97802
< 0.1%
97701
 
< 0.1%

cap_life_insurance_fixed_cap
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct130
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.26004364
Minimum0
Maximum220000
Zeros63522
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size497.8 KiB
2022-03-20T00:51:38.509573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum220000
Range220000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2538.927213
Coefficient of variation (CV)32.4421901
Kurtosis2875.610608
Mean78.26004364
Median Absolute Deviation (MAD)0
Skewness48.2726678
Sum4984930
Variance6446151.395
MonotonicityNot monotonic
2022-03-20T00:51:38.715743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
063522
99.7%
500007
 
< 0.1%
400006
 
< 0.1%
1000006
 
< 0.1%
50005
 
< 0.1%
37205
 
< 0.1%
24805
 
< 0.1%
100004
 
< 0.1%
247904
 
< 0.1%
20303
 
< 0.1%
Other values (120)130
 
0.2%
ValueCountFrequency (%)
063522
99.7%
901
 
< 0.1%
2501
 
< 0.1%
4301
 
< 0.1%
4601
 
< 0.1%
4901
 
< 0.1%
5301
 
< 0.1%
5802
 
< 0.1%
5901
 
< 0.1%
6202
 
< 0.1%
ValueCountFrequency (%)
2200001
 
< 0.1%
2000001
 
< 0.1%
1710501
 
< 0.1%
1500001
 
< 0.1%
1400001
 
< 0.1%
1278801
 
< 0.1%
1250001
 
< 0.1%
1170001
 
< 0.1%
1040001
 
< 0.1%
1000006
< 0.1%

cap_life_insurance_decreasing_cap
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2032
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11565.57059
Minimum0
Maximum780000
Zeros56589
Zeros (%)88.8%
Negative0
Negative (%)0.0%
Memory size497.8 KiB
2022-03-20T00:51:38.928792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile100000
Maximum780000
Range780000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40229.76173
Coefficient of variation (CV)3.478407002
Kurtosis29.2956242
Mean11565.57059
Median Absolute Deviation (MAD)0
Skewness4.63260835
Sum736692150
Variance1618433729
MonotonicityNot monotonic
2022-03-20T00:51:39.145302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
056589
88.8%
100000234
 
0.4%
50000173
 
0.3%
150000162
 
0.3%
125000156
 
0.2%
75000143
 
0.2%
40000137
 
0.2%
80000128
 
0.2%
200000113
 
0.2%
90000101
 
0.2%
Other values (2022)5761
 
9.0%
ValueCountFrequency (%)
056589
88.8%
3101
 
< 0.1%
3401
 
< 0.1%
4801
 
< 0.1%
5201
 
< 0.1%
5301
 
< 0.1%
7001
 
< 0.1%
9001
 
< 0.1%
9301
 
< 0.1%
9601
 
< 0.1%
ValueCountFrequency (%)
7800001
< 0.1%
7400002
< 0.1%
6500001
< 0.1%
6000001
< 0.1%
5755001
< 0.1%
5665001
< 0.1%
5250001
< 0.1%
5200001
< 0.1%
5000001
< 0.1%
4910001
< 0.1%

prem_fire_car_other_insurance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct281
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.6670487
Minimum0
Maximum3000
Zeros43753
Zeros (%)68.7%
Negative0
Negative (%)0.0%
Memory size497.8 KiB
2022-03-20T00:51:39.370917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3240
95-th percentile980
Maximum3000
Range3000
Interquartile range (IQR)240

Descriptive statistics

Standard deviation368.5996521
Coefficient of variation (CV)2.006890483
Kurtosis7.920696209
Mean183.6670487
Median Absolute Deviation (MAD)0
Skewness2.604311701
Sum11699040
Variance135865.7035
MonotonicityNot monotonic
2022-03-20T00:51:39.743025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
043753
68.7%
60421
 
0.7%
70410
 
0.6%
90399
 
0.6%
80380
 
0.6%
380305
 
0.5%
340291
 
0.5%
370289
 
0.5%
420285
 
0.4%
330282
 
0.4%
Other values (271)16882
 
26.5%
ValueCountFrequency (%)
043753
68.7%
1010
 
< 0.1%
2044
 
0.1%
3073
 
0.1%
4064
 
0.1%
5091
 
0.1%
60421
 
0.7%
70410
 
0.6%
80380
 
0.6%
90399
 
0.6%
ValueCountFrequency (%)
30001
< 0.1%
29601
< 0.1%
29301
< 0.1%
29001
< 0.1%
28802
< 0.1%
28302
< 0.1%
28101
< 0.1%
28001
< 0.1%
27901
< 0.1%
27702
< 0.1%

bal_personal_loan
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1512
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean402.4693471
Minimum0
Maximum71690
Zeros61047
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size497.8 KiB
2022-03-20T00:51:39.950523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum71690
Range71690
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2662.560352
Coefficient of variation (CV)6.615560591
Kurtosis144.7193861
Mean402.4693471
Median Absolute Deviation (MAD)0
Skewness10.31542097
Sum25636090
Variance7089227.626
MonotonicityNot monotonic
2022-03-20T00:51:40.155399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
061047
95.8%
9208
 
< 0.1%
27507
 
< 0.1%
107907
 
< 0.1%
100007
 
< 0.1%
25207
 
< 0.1%
72307
 
< 0.1%
25807
 
< 0.1%
39107
 
< 0.1%
27706
 
< 0.1%
Other values (1502)2587
 
4.1%
ValueCountFrequency (%)
061047
95.8%
1103
 
< 0.1%
1301
 
< 0.1%
1503
 
< 0.1%
1604
 
< 0.1%
1701
 
< 0.1%
1801
 
< 0.1%
1902
 
< 0.1%
2001
 
< 0.1%
2101
 
< 0.1%
ValueCountFrequency (%)
716901
< 0.1%
704001
< 0.1%
691601
< 0.1%
658101
< 0.1%
610701
< 0.1%
603301
< 0.1%
599601
< 0.1%
590301
< 0.1%
584101
< 0.1%
571201
< 0.1%

bal_mortgage_loan
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct5369
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8868.815486
Minimum0
Maximum490000
Zeros57449
Zeros (%)90.2%
Negative0
Negative (%)0.0%
Memory size497.8 KiB
2022-03-20T00:51:40.362185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile71598
Maximum490000
Range490000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation35525.2567
Coefficient of variation (CV)4.00563714
Kurtosis33.03241753
Mean8868.815486
Median Absolute Deviation (MAD)0
Skewness5.245175199
Sum564916940
Variance1262043863
MonotonicityNot monotonic
2022-03-20T00:51:40.590086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
057449
90.2%
489405
 
< 0.1%
1442405
 
< 0.1%
224204
 
< 0.1%
811304
 
< 0.1%
268804
 
< 0.1%
1500004
 
< 0.1%
11904
 
< 0.1%
1085704
 
< 0.1%
242304
 
< 0.1%
Other values (5359)6210
 
9.7%
ValueCountFrequency (%)
057449
90.2%
1401
 
< 0.1%
1701
 
< 0.1%
2001
 
< 0.1%
2501
 
< 0.1%
2601
 
< 0.1%
3201
 
< 0.1%
3302
 
< 0.1%
3401
 
< 0.1%
3501
 
< 0.1%
ValueCountFrequency (%)
4900001
< 0.1%
4869901
< 0.1%
4866201
< 0.1%
4850401
< 0.1%
4641501
< 0.1%
4520201
< 0.1%
4477201
< 0.1%
4424801
< 0.1%
4403601
< 0.1%
4354301
< 0.1%

bal_current_account
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1721
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1323.660612
Minimum-1000
Maximum20000
Zeros36146
Zeros (%)56.7%
Negative348
Negative (%)0.5%
Memory size497.8 KiB
2022-03-20T00:51:40.809303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1000
5-th percentile0
Q10
median0
Q31520
95-th percentile7140
Maximum20000
Range21000
Interquartile range (IQR)1520

Descriptive statistics

Standard deviation2685.727023
Coefficient of variation (CV)2.029014839
Kurtosis9.636095574
Mean1323.660612
Median Absolute Deviation (MAD)0
Skewness2.911212365
Sum84313210
Variance7213129.643
MonotonicityNot monotonic
2022-03-20T00:51:41.016136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
036146
56.7%
10330
 
0.5%
20241
 
0.4%
60193
 
0.3%
40173
 
0.3%
50173
 
0.3%
30171
 
0.3%
80162
 
0.3%
100144
 
0.2%
70131
 
0.2%
Other values (1711)25833
40.6%
ValueCountFrequency (%)
-10001
< 0.1%
-9502
< 0.1%
-9401
< 0.1%
-9301
< 0.1%
-9201
< 0.1%
-8901
< 0.1%
-8801
< 0.1%
-8502
< 0.1%
-8401
< 0.1%
-8201
< 0.1%
ValueCountFrequency (%)
200001
< 0.1%
198601
< 0.1%
198401
< 0.1%
198001
< 0.1%
197901
< 0.1%
196901
< 0.1%
195501
< 0.1%
193601
< 0.1%
193501
< 0.1%
192801
< 0.1%

bal_pension_saving
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct934
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean233.4219822
Minimum0
Maximum41170
Zeros62389
Zeros (%)97.9%
Negative0
Negative (%)0.0%
Memory size497.8 KiB
2022-03-20T00:51:41.228255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum41170
Range41170
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2140.173418
Coefficient of variation (CV)9.168688391
Kurtosis166.1891569
Mean233.4219822
Median Absolute Deviation (MAD)0
Skewness12.07722778
Sum14868280
Variance4580342.26
MonotonicityNot monotonic
2022-03-20T00:51:41.442868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
062389
97.9%
624011
 
< 0.1%
62308
 
< 0.1%
42608
 
< 0.1%
9107
 
< 0.1%
121107
 
< 0.1%
32106
 
< 0.1%
22106
 
< 0.1%
71206
 
< 0.1%
9305
 
< 0.1%
Other values (924)1244
 
2.0%
ValueCountFrequency (%)
062389
97.9%
1201
 
< 0.1%
1502
 
< 0.1%
1601
 
< 0.1%
2101
 
< 0.1%
2303
 
< 0.1%
3001
 
< 0.1%
3101
 
< 0.1%
3501
 
< 0.1%
3802
 
< 0.1%
ValueCountFrequency (%)
411701
< 0.1%
406001
< 0.1%
405101
< 0.1%
404301
< 0.1%
403201
< 0.1%
402301
< 0.1%
401101
< 0.1%
399401
< 0.1%
398501
< 0.1%
397501
< 0.1%

bal_savings_account
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct4936
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17872.48489
Minimum0
Maximum50000
Zeros1930
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size497.8 KiB
2022-03-20T00:51:41.653128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3638
Q18450
median15170
Q325480
95-th percentile41140
Maximum50000
Range50000
Interquartile range (IQR)17030

Descriptive statistics

Standard deviation11750.86847
Coefficient of variation (CV)0.6574837544
Kurtosis-0.3025699008
Mean17872.48489
Median Absolute Deviation (MAD)7890
Skewness0.7195023378
Sum1138423670
Variance138082909.7
MonotonicityNot monotonic
2022-03-20T00:51:41.903564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01930
 
3.0%
15000218
 
0.3%
25000185
 
0.3%
10110165
 
0.3%
20000160
 
0.3%
10000148
 
0.2%
10060136
 
0.2%
5000129
 
0.2%
10100108
 
0.2%
30000107
 
0.2%
Other values (4926)60411
94.8%
ValueCountFrequency (%)
01930
3.0%
1025
 
< 0.1%
2016
 
< 0.1%
3010
 
< 0.1%
4011
 
< 0.1%
506
 
< 0.1%
608
 
< 0.1%
706
 
< 0.1%
806
 
< 0.1%
906
 
< 0.1%
ValueCountFrequency (%)
5000079
0.1%
499901
 
< 0.1%
499703
 
< 0.1%
499606
 
< 0.1%
499501
 
< 0.1%
499402
 
< 0.1%
499302
 
< 0.1%
499003
 
< 0.1%
498901
 
< 0.1%
498701
 
< 0.1%

bal_savings_account_starter
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct334
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.64117619
Minimum0
Maximum24050
Zeros63326
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size497.8 KiB
2022-03-20T00:51:42.136324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum24050
Range24050
Interquartile range (IQR)0

Descriptive statistics

Standard deviation892.9598587
Coefficient of variation (CV)15.49170086
Kurtosis363.4034392
Mean57.64117619
Median Absolute Deviation (MAD)0
Skewness18.23536083
Sum3671570
Variance797377.3092
MonotonicityNot monotonic
2022-03-20T00:51:42.367777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
063326
99.4%
100004
 
< 0.1%
140003
 
< 0.1%
50103
 
< 0.1%
110003
 
< 0.1%
30003
 
< 0.1%
134103
 
< 0.1%
8203
 
< 0.1%
76502
 
< 0.1%
155802
 
< 0.1%
Other values (324)345
 
0.5%
ValueCountFrequency (%)
063326
99.4%
101
 
< 0.1%
701
 
< 0.1%
801
 
< 0.1%
1201
 
< 0.1%
1601
 
< 0.1%
2601
 
< 0.1%
3301
 
< 0.1%
5201
 
< 0.1%
5701
 
< 0.1%
ValueCountFrequency (%)
240501
< 0.1%
240001
< 0.1%
238001
< 0.1%
234801
< 0.1%
231301
< 0.1%
224701
< 0.1%
224401
< 0.1%
224101
< 0.1%
223301
< 0.1%
220401
< 0.1%

bal_current_account_starter
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct414
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.32089423
Minimum-330
Maximum19790
Zeros62798
Zeros (%)98.6%
Negative2
Negative (%)< 0.1%
Memory size497.8 KiB
2022-03-20T00:51:42.602798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-330
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum19790
Range20120
Interquartile range (IQR)0

Descriptive statistics

Standard deviation407.8778925
Coefficient of variation (CV)13.45204034
Kurtosis579.1917055
Mean30.32089423
Median Absolute Deviation (MAD)0
Skewness21.12371347
Sum1931350
Variance166364.3752
MonotonicityNot monotonic
2022-03-20T00:51:42.821910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
062798
98.6%
1029
 
< 0.1%
2023
 
< 0.1%
5016
 
< 0.1%
4014
 
< 0.1%
3012
 
< 0.1%
10011
 
< 0.1%
809
 
< 0.1%
2108
 
< 0.1%
1208
 
< 0.1%
Other values (404)769
 
1.2%
ValueCountFrequency (%)
-3301
 
< 0.1%
-1701
 
< 0.1%
062798
98.6%
1029
 
< 0.1%
2023
 
< 0.1%
3012
 
< 0.1%
4014
 
< 0.1%
5016
 
< 0.1%
607
 
< 0.1%
703
 
< 0.1%
ValueCountFrequency (%)
197901
< 0.1%
171201
< 0.1%
150301
< 0.1%
147801
< 0.1%
147501
< 0.1%
145301
< 0.1%
140201
< 0.1%
137901
< 0.1%
134701
< 0.1%
128701
< 0.1%

visits_distinct_so
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.230199224
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size497.8 KiB
2022-03-20T00:51:43.020321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5014984575
Coefficient of variation (CV)0.4076562947
Kurtosis7.638635554
Mean1.230199224
Median Absolute Deviation (MAD)0
Skewness2.459796736
Sum78360
Variance0.2515007029
MonotonicityNot monotonic
2022-03-20T00:51:43.167381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
151017
80.1%
211018
 
17.3%
31392
 
2.2%
4229
 
0.4%
532
 
0.1%
68
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
151017
80.1%
211018
 
17.3%
31392
 
2.2%
4229
 
0.4%
532
 
0.1%
68
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
68
 
< 0.1%
532
 
0.1%
4229
 
0.4%
31392
 
2.2%
211018
 
17.3%
151017
80.1%

visits_distinct_so_areas
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.042607972
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size497.8 KiB
2022-03-20T00:51:43.495727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2249913464
Coefficient of variation (CV)0.2157966871
Kurtosis51.61021735
Mean1.042607972
Median Absolute Deviation (MAD)0
Skewness6.276163857
Sum66411
Variance0.05062110594
MonotonicityNot monotonic
2022-03-20T00:51:43.652540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
161246
96.2%
22227
 
3.5%
3194
 
0.3%
423
 
< 0.1%
55
 
< 0.1%
62
 
< 0.1%
ValueCountFrequency (%)
161246
96.2%
22227
 
3.5%
3194
 
0.3%
423
 
< 0.1%
55
 
< 0.1%
62
 
< 0.1%
ValueCountFrequency (%)
62
 
< 0.1%
55
 
< 0.1%
423
 
< 0.1%
3194
 
0.3%
22227
 
3.5%
161246
96.2%

customer_since_all
Categorical

HIGH CARDINALITY

Distinct661
Distinct (%)1.0%
Missing234
Missing (%)0.4%
Memory size497.8 KiB
1981-01
 
3050
2017-02
 
744
2017-01
 
667
1994-06
 
610
2017-05
 
532
Other values (656)
57860 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)< 0.1%

Sample

1st row1983-03
2nd row2017-01
3rd row1980-12
4th row1998-08
5th row2012-11

Common Values

ValueCountFrequency (%)
1981-013050
 
4.8%
2017-02744
 
1.2%
2017-01667
 
1.0%
1994-06610
 
1.0%
2017-05532
 
0.8%
2016-10486
 
0.8%
1995-04457
 
0.7%
2002-11422
 
0.7%
1994-07421
 
0.7%
1991-12404
 
0.6%
Other values (651)55670
87.4%

Length

2022-03-20T00:51:43.816100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1981-013050
 
4.8%
2017-02744
 
1.2%
2017-01667
 
1.1%
1994-06610
 
1.0%
2017-05532
 
0.8%
2016-10486
 
0.8%
1995-04457
 
0.7%
2002-11422
 
0.7%
1994-07421
 
0.7%
1991-12404
 
0.6%
Other values (651)55670
87.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

customer_since_bank
Categorical

HIGH CARDINALITY

Distinct577
Distinct (%)0.9%
Missing249
Missing (%)0.4%
Memory size497.8 KiB
1981-01
 
3181
2017-02
 
1158
2017-01
 
1015
2017-05
 
794
2016-10
 
768
Other values (572)
56532 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st row1994-08
2nd row2017-01
3rd row1980-12
4th row2013-10
5th row2012-11

Common Values

ValueCountFrequency (%)
1981-013181
 
5.0%
2017-021158
 
1.8%
2017-011015
 
1.6%
2017-05794
 
1.2%
2016-10768
 
1.2%
1994-06663
 
1.0%
2013-10533
 
0.8%
2002-11503
 
0.8%
1995-04478
 
0.8%
1994-07438
 
0.7%
Other values (567)53917
84.6%

Length

2022-03-20T00:51:43.961355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1981-013181
 
5.0%
2017-021158
 
1.8%
2017-011015
 
1.6%
2017-05794
 
1.3%
2016-10768
 
1.2%
1994-06663
 
1.0%
2013-10533
 
0.8%
2002-11503
 
0.8%
1995-04478
 
0.8%
1994-07438
 
0.7%
Other values (567)53917
85.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

customer_gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
1
32712 
2
30985 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
132712
51.4%
230985
48.6%

Length

2022-03-20T00:51:44.118812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-20T00:51:44.215864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
132712
51.4%
230985
48.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

customer_birth_date
Categorical

HIGH CARDINALITY

Distinct1092
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
1952-03
 
132
1952-04
 
127
1966-05
 
125
1951-07
 
122
1956-07
 
122
Other values (1087)
63069 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)0.1%

Sample

1st row1943-09
2nd row1994-02
3rd row1936-10
4th row1946-09
5th row1996-04

Common Values

ValueCountFrequency (%)
1952-03132
 
0.2%
1952-04127
 
0.2%
1966-05125
 
0.2%
1951-07122
 
0.2%
1956-07122
 
0.2%
1943-07117
 
0.2%
1951-03116
 
0.2%
1955-01114
 
0.2%
1952-01113
 
0.2%
1956-08112
 
0.2%
Other values (1082)62497
98.1%

Length

2022-03-20T00:51:44.321199image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1952-03132
 
0.2%
1952-04127
 
0.2%
1966-05125
 
0.2%
1951-07122
 
0.2%
1956-07122
 
0.2%
1943-07117
 
0.2%
1951-03116
 
0.2%
1955-01114
 
0.2%
1952-01113
 
0.2%
1956-08112
 
0.2%
Other values (1082)62497
98.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

customer_postal_code
Real number (ℝ≥0)

Distinct1034
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5577.261959
Minimum0
Maximum9992
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size497.8 KiB
2022-03-20T00:51:44.502425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1400
Q12650
median4877
Q38750
95-th percentile9810
Maximum9992
Range9992
Interquartile range (IQR)6100

Descriptive statistics

Standard deviation3020.064554
Coefficient of variation (CV)0.5414959125
Kurtosis-1.620230611
Mean5577.261959
Median Absolute Deviation (MAD)2817
Skewness0.05374443809
Sum355254855
Variance9120789.908
MonotonicityNot monotonic
2022-03-20T00:51:44.721084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9120540
 
0.8%
9100523
 
0.8%
8800522
 
0.8%
2800452
 
0.7%
2100414
 
0.6%
9940394
 
0.6%
2960388
 
0.6%
8400386
 
0.6%
3590375
 
0.6%
3740341
 
0.5%
Other values (1024)59362
93.2%
ValueCountFrequency (%)
010
 
< 0.1%
1000104
 
0.2%
1020114
 
0.2%
1030265
0.4%
104088
 
0.1%
1050155
0.2%
106069
 
0.1%
1070324
0.5%
1080184
0.3%
108141
 
0.1%
ValueCountFrequency (%)
99923
 
< 0.1%
999191
 
0.1%
9990322
0.5%
998810
 
< 0.1%
998214
 
< 0.1%
99818
 
< 0.1%
9980107
 
0.2%
997136
 
0.1%
997041
 
0.1%
9968116
 
0.2%

customer_occupation_code
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)< 0.1%
Missing2002
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean8.77353108
Minimum0
Maximum9
Zeros421
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size497.8 KiB
2022-03-20T00:51:44.896291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q19
median9
Q39
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.131452913
Coefficient of variation (CV)0.128962091
Kurtosis31.64044668
Mean8.77353108
Median Absolute Deviation (MAD)0
Skewness-5.485065882
Sum541283
Variance1.280185695
MonotonicityNot monotonic
2022-03-20T00:51:45.041017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
958836
92.4%
41639
 
2.6%
0421
 
0.7%
8318
 
0.5%
6183
 
0.3%
5153
 
0.2%
7104
 
0.2%
124
 
< 0.1%
310
 
< 0.1%
27
 
< 0.1%
(Missing)2002
 
3.1%
ValueCountFrequency (%)
0421
 
0.7%
124
 
< 0.1%
27
 
< 0.1%
310
 
< 0.1%
41639
 
2.6%
5153
 
0.2%
6183
 
0.3%
7104
 
0.2%
8318
 
0.5%
958836
92.4%
ValueCountFrequency (%)
958836
92.4%
8318
 
0.5%
7104
 
0.2%
6183
 
0.3%
5153
 
0.2%
41639
 
2.6%
310
 
< 0.1%
27
 
< 0.1%
124
 
< 0.1%
0421
 
0.7%

customer_self_employed
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
0
58154 
1
 
5543

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
058154
91.3%
15543
 
8.7%

Length

2022-03-20T00:51:45.187892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-20T00:51:45.291770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
058154
91.3%
15543
 
8.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

customer_education
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing47125
Missing (%)74.0%
Infinite0
Infinite (%)0.0%
Mean2.463734009
Minimum0
Maximum6
Zeros2178
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size497.8 KiB
2022-03-20T00:51:45.377534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.520308961
Coefficient of variation (CV)0.6170751206
Kurtosis-0.4347524722
Mean2.463734009
Median Absolute Deviation (MAD)1
Skewness0.2069922379
Sum40829
Variance2.311339336
MonotonicityNot monotonic
2022-03-20T00:51:45.507611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
35015
 
7.9%
24506
 
7.1%
02178
 
3.4%
52064
 
3.2%
11802
 
2.8%
4696
 
1.1%
6311
 
0.5%
(Missing)47125
74.0%
ValueCountFrequency (%)
02178
3.4%
11802
 
2.8%
24506
7.1%
35015
7.9%
4696
 
1.1%
52064
3.2%
6311
 
0.5%
ValueCountFrequency (%)
6311
 
0.5%
52064
3.2%
4696
 
1.1%
35015
7.9%
24506
7.1%
11802
 
2.8%
02178
3.4%

customer_children
Categorical

HIGH CORRELATION
MISSING

Distinct8
Distinct (%)< 0.1%
Missing23364
Missing (%)36.7%
Memory size497.8 KiB
no
22886 
mature
4849 
adolescent
3912 
young
2652 
preschool
2322 
Other values (3)
3712 

Length

Max length10
Median length2
Mean length4.283737882
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmature
2nd rowmature
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no22886
35.9%
mature4849
 
7.6%
adolescent3912
 
6.1%
young2652
 
4.2%
preschool2322
 
3.6%
grownup1908
 
3.0%
onebaby1466
 
2.3%
yes338
 
0.5%
(Missing)23364
36.7%

Length

2022-03-20T00:51:45.682182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-20T00:51:45.803702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
no22886
56.7%
mature4849
 
12.0%
adolescent3912
 
9.7%
young2652
 
6.6%
preschool2322
 
5.8%
grownup1908
 
4.7%
onebaby1466
 
3.6%
yes338
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

customer_relationship
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing14899
Missing (%)23.4%
Memory size497.8 KiB
couple
36179 
single
12619 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcouple
2nd rowsingle
3rd rowcouple
4th rowcouple
5th rowcouple

Common Values

ValueCountFrequency (%)
couple36179
56.8%
single12619
 
19.8%
(Missing)14899
23.4%

Length

2022-03-20T00:51:45.958448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-20T00:51:46.051809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
couple36179
74.1%
single12619
 
25.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

target
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
0
61784 
1
 
1913

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
061784
97.0%
11913
 
3.0%

Length

2022-03-20T00:51:46.160294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-20T00:51:46.259679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
061784
97.0%
11913
 
3.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-03-20T00:51:26.785086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:28.509413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:32.041113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:35.356503image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:38.832169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:42.757577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:47.904666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:51.594194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:55.100283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:58.497047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:02.048721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:05.710722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:09.309578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:13.066406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:16.409846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:19.955238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:23.339979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:26.971037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:28.702241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:32.227008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:35.540819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:39.040942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:42.996588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:48.108863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:51.776455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:55.290759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:58.678221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:02.240698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:05.920466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:09.500835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:13.261129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:16.604621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:20.150344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:23.525390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:27.154019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:28.897927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:32.420139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:35.936284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:39.243143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:43.454517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:48.327295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:51.985537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:55.488458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:58.877614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:02.452865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:06.122219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:09.713117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:13.453481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:16.804824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:20.346224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:23.721981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:27.340697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:29.101615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:32.622836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:36.114869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:39.440680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:43.684887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:48.526431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:52.174986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:55.668486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:59.061538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:02.648673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:06.313404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:09.915289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:13.647142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:16.987776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:20.538551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:23.902515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:27.547564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:29.326058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:32.843387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:36.327964image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:39.671329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:43.978245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:48.744979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:52.381439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:55.889838image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:59.263904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:02.864818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:06.530639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:10.145655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:13.871422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:17.204877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:20.759723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:24.302070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:27.743447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:29.527259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:33.047544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:36.530672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:39.888092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:44.478664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:48.967164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:52.594828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:56.092524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:59.471917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:03.094388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:06.737208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:10.364360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:14.080461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:17.411435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:20.968150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:24.502732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:51:27.945262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:29.732706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:33.247030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:36.737283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:40.102053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:44.872822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:49.359661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-20T00:50:52.797206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
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Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
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Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
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Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
2022-03-20T00:51:32.554276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-03-20T00:51:33.400575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-03-20T00:51:33.807722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

client_idhomebanking_activehas_homebankinghas_insurance_21has_insurance_23has_life_insurance_fixed_caphas_life_insurance_decreasing_caphas_fire_car_other_insurancehas_personal_loanhas_mortgage_loanhas_current_accounthas_pension_savinghas_savings_accounthas_savings_account_starterhas_current_account_starterbal_insurance_21bal_insurance_23cap_life_insurance_fixed_capcap_life_insurance_decreasing_capprem_fire_car_other_insurancebal_personal_loanbal_mortgage_loanbal_current_accountbal_pension_savingbal_savings_accountbal_savings_account_starterbal_current_account_startervisits_distinct_sovisits_distinct_so_areascustomer_since_allcustomer_since_bankcustomer_gendercustomer_birth_datecustomer_postal_codecustomer_occupation_codecustomer_self_employedcustomer_educationcustomer_childrencustomer_relationshiptarget
0910df42ad36243aa4ce16324cd7b15b00000001001010000002000590022000001.01.01983-031994-0811943-0936309.000.0NaNNaN0
14e19dc3a54323c5bbfc374664b950cd1110000000101000000000940010570001.01.02017-012017-0111994-0224609.00NaNmaturecouple0
2f5d08db1b86c0cb0f566bf446cff1fb4110000100101000000320001210015200001.01.01980-121980-1221936-1026609.00NaNNaNsingle0
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5f7bae3a0fefd323ecf7d4a2fab4e7826110000000100010000000415000041501.01.02004-082015-0821963-111430NaN0NaNnocouple0
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95c9b1a4b7b9255d5d9d9fac3328a26a400000010010100000070001357008850001.01.01975-011996-0711938-0122209.00NaNnocouple0

Last rows

client_idhomebanking_activehas_homebankinghas_insurance_21has_insurance_23has_life_insurance_fixed_caphas_life_insurance_decreasing_caphas_fire_car_other_insurancehas_personal_loanhas_mortgage_loanhas_current_accounthas_pension_savinghas_savings_accounthas_savings_account_starterhas_current_account_starterbal_insurance_21bal_insurance_23cap_life_insurance_fixed_capcap_life_insurance_decreasing_capprem_fire_car_other_insurancebal_personal_loanbal_mortgage_loanbal_current_accountbal_pension_savingbal_savings_accountbal_savings_account_starterbal_current_account_startervisits_distinct_sovisits_distinct_so_areascustomer_since_allcustomer_since_bankcustomer_gendercustomer_birth_datecustomer_postal_codecustomer_occupation_codecustomer_self_employedcustomer_educationcustomer_childrencustomer_relationshiptarget
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63689340a31bb1a3af30d7058e0b2c2fa4c26000000000001000000000006600001.01.02008-122008-1221935-0243409.00NaNnoNaN0
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636917d13effc553a8e1a03f40470f8c3ae9111000100110100000133000001626002980016240002.01.01997-061997-0611980-0385209.011.0preschoolcouple0
636920a58f2eb841ddac0626dacac6ca6952411100000010100242000000080017060002.01.01998-061998-0621982-0280009.00NaNnocouple0
63693193be2222be99bf04f42193b5cdfb95d00100010110100781000011800130302690022400002.01.01989-051989-0521965-0520209.001.0NaNNaN0
63694fa9f074ec8cad610ccaec2270021490e010001101101000001860005200179550292006820003.01.01991-011991-0121976-1110709.003.0NaNsingle0
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63696977dda870c3f54df46297df3869b29071100000001010000000006890013540001.01.02002-022002-0211995-0391009.00NaNnoNaN0